Tootfinder

Bochner-type theorems for distributional category
Ekansh Jauhari, John Oprea
https://arxiv.org/abs/2505.21763 https://arxiv.org/pdf/2…

Bochner-type theorems for distributional category
We show that in the presence of a geometric condition such as non-negative Ricci curvature, the distributional category of a manifold may be used to bound invariants, such as the first Betti number and macroscopic dimension, from above. Moreover, à la Bochner, when the bound is an equality, special constraints are imposed on the manifold. We show that the distributional category of a space also bounds the rank of the Gottlieb group, with equality imposing constraints on the fundamental group. …

Updated constraints on the primordial power spectrum at sub-Mpc scales
Torsten Bringmann, Djuna Croon, Sergio Sevillano Mu\~noz
https://arxiv.org/abs/2506.20704

Updated constraints on the primordial power spectrum at sub-Mpc scales
The primordial power spectrum of matter density perturbations contains highly valuable information about new fundamental physics, in particular cosmological inflation, but is only very weakly constrained observationally for small cosmological scales $k\gtrsim 3\,{\rm Mpc}^{-1}$. We derive novel constraints, $\mathcal{P}_\mathcal{R}(k)\lesssim 5\cdot10^{-6}$ over a large range of such scales, from the formation of ultracompact minihalos in the early universe. Unlike most existing constraints of …

This https://arxiv.org/abs/2406.03674 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDS_…

Learning Safe Strategies for Value Maximizing Buyers in Uniform Price Auctions
We study the bidding problem in repeated uniform price multi-unit auctions from the perspective of a single value-maximizing buyer who aims to maximize their cumulative value over $T$ rounds while adhering to return-on-investment (RoI) constraints in each round. Buyers adopt $m$-uniform bidding format, where they submit $m$ bid-quantity pairs $(b_i, q_i)$ to demand $q_i$ units at bid $b_i$. We introduce safe bidding strategies as those that satisfy RoI constraints in every auction, regardless o…

Constraints on the strength of first-order phase transition in the low density region
Bikai Gao
https://arxiv.org/abs/2505.21970 https://

Constraints on the strength of first-order phase transition in the low density region
We systematically studied the first-order phase transitions in the low-density region of neutron star matter using a unified framework combining the parity doublet model for hadronic matter, an NJL-type quark model for high-density quark matter, and a model-independent intermediate region treatment. By confronting with recent neutron star observation constraints, we find it possible to have the early phase transitions (PT) at low densities ($n_B \leq 2n_0$) in a way consistent with existing neu…

Yearly Subscription:
- $8 per Month VAT once -> $114.24 at once
Monthly Subscription:
- $14 per Month VAT monthly -> $199,92 over 12 payments
I prefer monthly payments because they don't take a big chunk out of my budget and I have control to cancel if I want. But that costs extra. I get the constraints and why that model exists but I hate it.

This https://arxiv.org/abs/2402.00719 has been replaced.
link: https://scholar.google.com/scholar?q=a

Geometric Contact Potential
Barrier potentials gained popularity as a means for robust contact handling in physical modeling and for modeling self-avoiding shapes. The key to the success of these approaches is adherence to geometric constraints, i.e., avoiding intersections, which are the cause of most robustness problems in complex deformation simulation with contact. However, existing barrier-potential methods may lead to spurious forces and imperfect satisfaction of the geometric constraints. They may have strong resol…

First-order methods for stochastic and finite-sum convex optimization with deterministic constraints
Zhaosong Lu, Yifeng Xiao
https://arxiv.org/abs/2506.20630

First-order methods for stochastic and finite-sum convex optimization with deterministic constraints
In this paper, we study a class of stochastic and finite-sum convex optimization problems with deterministic constraints. Existing methods typically aim to find an $ε$-$expectedly\ feasible\ stochastic\ optimal$ solution, in which the expected constraint violation and expected optimality gap are both within a prescribed tolerance $ε$. However, in many practical applications, constraints must be nearly satisfied with certainty, rendering such solutions potentially unsuitable due to the risk of…

Electric dipole polarizability constraints on neutron skin and symmetry energy
P. von Neumann-Cosel (Institut f\"ur Kernphysik, Technische Universit\"at Darmstadt, Germany, Norwegian Nuclear Research Center and Department of Physics, University of Oslo, Norway), A. Tamii (Research Center for Nuclear Physics, University of Osaka, Japan)
http…

Electric dipole polarizability constraints on neutron skin and symmetry energy
We review the experimental knowledge on the dipole polarizability (DP) of nuclei and its relation to the neutron skin thickness and properties of the neutron-rich matter equation of state (EOS). The discussion focuses on recent experiments using relativistic Coulomb excitation in inelastic proton scattering at extreme forward angles covering a mass range from $^{40}$Ca to $^{208}$Pb. Constraints on the neutron skins and the density dependence of the symmetry energy are derived from systematic c…

Constraints on the dark sector from electroweak precision observables
B. M. Loizos, X. G. Wang, A. W. Thomas, M. J. White, A. G. Williams
https://arxiv.org/abs/2506.20080

Constraints on the dark sector from electroweak precision observables
We revisit the Standard Model fit to electroweak precision observables in using the latest data and the Particle Data Group (PDG) measurement of the W boson mass. The analysis is then repeated in light of the new W boson mass measurement from the Collider Detector at Fermilab (CDF) collaboration. We then introduce a dark photon to the model, placing constraints on the parameter space arising from these electroweak precision observables, both for the PDG and CDF values for the W boson mass. We a…

Constraints on the symmetric mass generation paradigm for lattice chiral gauge theories
Maarten Golterman, Yigal Shamir
https://arxiv.org/abs/2505.20436 ht…

Constraints on the symmetric mass generation paradigm for lattice chiral gauge theories
Within the symmetric mass generation (SMG) approach to the construction of lattice chiral gauge theories, one attempts to use interactions to render mirror fermions massive without symmetry breaking, thus obtaining the desired chiral massless spectrum. If successful, the gauge field can be turned on, and thus a chiral gauge theory can be constructed in the phase in which SMG takes place. In this paper we argue that the zeros that often replace the mirror poles of fermion two-point functions in …

Sampling conditioned diffusions via Pathspace Projected Monte Carlo
Tobias Grafke
https://arxiv.org/abs/2506.15743 https://arxiv.org/…

Sampling conditioned diffusions via Pathspace Projected Monte Carlo
We present an algorithm to sample stochastic differential equations conditioned on rather general constraints, including integral constraints, endpoint constraints, and stochastic integral constraints. The algorithm is a pathspace Metropolis-adjusted manifold sampling scheme, which samples stochastic paths on the submanifold of realizations that adhere to the conditioning constraint. We demonstrate the effectiveness of the algorithm by sampling a dynamical condensation phase transition, conditi…

This https://arxiv.org/abs/2107.04946 has been replaced.
link: https://scholar.google.com/scholar?q=a

Inference for the proportional odds cumulative logit model with monotonicity constraints for ordinal predictors and ordinal response
The proportional odds cumulative logit model (POCLM) is a standard regression model for an ordinal response. Ordinality of predictors can be incorporated by monotonicity constraints for the corresponding parameters. It is shown that estimators defined by optimization, such as maximum likelihood estimators, for an unconstrained model and for parameters in the interior set of the parameter space of a constrained model are asymptotically equivalent. This is used in order to derive asymptotic confi…

Planck Constraints on Axion-Like Particles through Isotropic Cosmic Birefringence
Toshiya Namikawa, Kai Murai, Fumihiro Naokawa
https://arxiv.org/abs/2506.20824

Planck Constraints on Axion-Like Particles through Isotropic Cosmic Birefringence
We present constraints on isotropic cosmic birefringence induced by axion-like particles (ALPs), derived from the analysis of cosmic microwave background (CMB) polarization measurements obtained with the high-frequency channels of Planck. Recent measurements report a hint of isotropic cosmic birefringence, though its origin remains uncertain. The detailed dynamics of ALPs can leave characteristic imprints on the shape of the $EB$ angular power spectrum, which can be exploited to constrain speci…

This https://arxiv.org/abs/2502.04053 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_hepe…

Proof of principle for a light dark matter search with low-energy positron beams at NA64
Thermal light dark matter (LDM) with particle masses in the 1 MeV - 1 GeV range could successfully explain the observed dark matter abundance as a relic from the primordial Universe. In this picture, a new feeble interaction acts as a "portal" between the Standard Model and LDM particles, allowing for the exploration of this paradigm at accelerator experiments. In the last years, the "missing energy" experiment NA64e at CERN SPS (Super Proton Synchrotron) has set world-leading constraints in th…

This https://arxiv.org/abs/2505.04364 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csMA_…

Benchmarking LLMs' Swarm intelligence
Large Language Models (LLMs) show potential for complex reasoning, yet their capacity for emergent coordination in Multi-Agent Systems (MAS) when operating under strict swarm-like constraints-limited local perception and communication-remains largely unexplored. Existing benchmarks often do not fully capture the unique challenges of decentralized coordination when agents operate with incomplete spatio-temporal information. To bridge this gap, we introduce SwarmBench, a novel benchmark designed …

This https://arxiv.org/abs/2503.17189 has been replaced.
initial toot: https://mastoxiv.page/@a…

Bayesian optimisation of poloidal field coil positions in tokamaks
The tokamak is a world-leading concept for producing sustainable energy via magnetically-confined nuclear fusion. Identifying where to position the magnets within a tokamak, specifically the poloidal field (PF) coils, is a design problem which requires balancing a number of competing economic, physical, and engineering objectives and constraints. In this paper, we show that multi-objective Bayesian optimisation (BO), an iterative optimisation technique utilising probabilistic machine learning m…

A Proof of the Hodge Conjecture for a Special Class of Calabi-Yau 5-Folds via Exceptional Group Constraints
Dongzhe Zheng
https://arxiv.org/abs/2506.17754 …

A Proof of the Hodge Conjecture for a Special Class of Calabi-Yau 5-Folds via Exceptional Group Constraints
This paper provides a complete proof of the (1,1)-type Hodge conjecture for a special class of 5-dimensional Calabi-Yau manifolds ($h^{1,1}=56$), while establishing and implementing a novel methodology based on exceptional group constraints. The core idea of this methodology is to replace the "geometric construction" that traditional methods rely on -- which becomes infeasible in high-dimensional cases -- with the "algebraic rigidity" of Lie algebra representation theory. We prove that by intro…

Mitigating Hallucination of Large Vision-Language Models via Dynamic Logits Calibration
Jiahe Chen, Jiaying He, Qian Shao, Qiyuan Chen, Jiahe Ying, Hongxia Xu, Jintai Chen, Jianwei Zheng, Jian Wu
https://arxiv.org/abs/2506.21509

Mitigating Hallucination of Large Vision-Language Models via Dynamic Logits Calibration
Large Vision-Language Models (LVLMs) have demonstrated significant advancements in multimodal understanding, yet they are frequently hampered by hallucination-the generation of text that contradicts visual input. Existing training-free decoding strategies exhibit critical limitations, including the use of static constraints that do not adapt to semantic drift during generation, inefficiency stemming from the need for multiple forward passes, and degradation of detail due to overly rigid interve…

Observational constraints on inflationary models with non-minimally derivative coupling by ACT
Qing Gao, Yanjiang Qian, Yungui Gong, Zhu Yi
https://arxiv.org/abs/2506.18456

Observational constraints on inflationary models with non-minimally derivative coupling by ACT
The most recent data release from the Atacama Cosmology Telescope (ACT) reveals a larger value of the scalar spectral tilt $n_s$, ruling out a broad class of inflationary attractors. In this paper, we consider inflationary models including the power law potential, the hilltop model, the polynomial $α$-attractor and exponential $α-$attractor, with non-minimally derivative coupling in the high friction limit, and show how the models can fit ACT data. We also derive constraints on the model para…

Dynamic Risk-Aware MPPI for Mobile Robots in Crowds via Efficient Monte Carlo Approximations
Elia Trevisan, Khaled A. Mustafa, Godert Notten, Xinwei Wang, Javier Alonso-Mora
https://arxiv.org/abs/2506.21205

Dynamic Risk-Aware MPPI for Mobile Robots in Crowds via Efficient Monte Carlo Approximations
Deploying mobile robots safely among humans requires the motion planner to account for the uncertainty in the other agents' predicted trajectories. This remains challenging in traditional approaches, especially with arbitrarily shaped predictions and real-time constraints. To address these challenges, we propose a Dynamic Risk-Aware Model Predictive Path Integral control (DRA-MPPI), a motion planner that incorporates uncertain future motions modelled with potentially non-Gaussian stochastic pre…

Continuous Map Matching to Paths under Travel Time Constraints
Yannick Bosch, Sabine Storandt
https://arxiv.org/abs/2506.18354 https://

Continuous Map Matching to Paths under Travel Time Constraints
In this paper, we study the problem of map matching with travel time constraints. Given a sequence of $k$ spatio-temporal measurements and an embedded path graph with travel time costs, the goal is to snap each measurement to a close-by location in the graph, such that consecutive locations can be reached from one another along the path within the timestamp difference of the respective measurements. This problem arises in public transit data processing as well as in map matching of movement tra…

Scalable GPU Performance Variability Analysis framework
Ankur Lahiry, Ayush Pokharel, Seth Ockerman, Amal Gueroudji, Line Pouchard, Tanzima Z. Islam
https://arxiv.org/abs/2506.20674

Scalable GPU Performance Variability Analysis framework
Analyzing large-scale performance logs from GPU profilers often requires terabytes of memory and hours of runtime, even for basic summaries. These constraints prevent timely insight and hinder the integration of performance analytics into automated workflows. Existing analysis tools typically process data sequentially, making them ill-suited for HPC workflows with growing trace complexity and volume. We introduce a distributed data analysis framework that scales with dataset size and compute av…

Multi-period Mean-Buffered Probability of Exceedance in Defined Contribution Portfolio Optimization
Duy-Minh Dang, Chang Chen
https://arxiv.org/abs/2505.22121

Multi-period Mean-Buffered Probability of Exceedance in Defined Contribution Portfolio Optimization
We investigate multi-period mean-risk portfolio optimization for long-horizon Defined Contribution plans, focusing on buffered Probability of Exceedance (bPoE), a more intuitive, dollar-based alternative to Conditional Value-at-Risk (CVaR). We formulate both pre-commitment and time-consistent Mean-bPoE and Mean-CVaR portfolio optimization problems under realistic investment constraints (e.g., no leverage, no short selling) and jump-diffusion dynamics. These formulations are naturally framed as …

OSTRICH2: Solver for Complex String Constraints
Matthew Hague, Denghang Hu, Artur Je\.z, Anthony W. Lin, Oliver Markgraf, Philipp R\"ummer, Zhilin Wu
https://arxiv.org/abs/2506.14363

OSTRICH2: Solver for Complex String Constraints
We present OSTRICH2, the latest evolution of the SMT solver OSTRICH for string constraints. OSTRICH2 supports a wide range of complex functions on strings and provides completeness guarantees for a substantial fragment of string constraints, including the straight-line fragment and the chain-free fragment. OSTRICH2 provides full support for the SMT-LIB theory of Unicode strings, extending the standard with several unique features not found in other solvers: among others, parsing of ECMAScript r…

Symmetry Sectors in Chord Space and Relational Holography in the DSSYK
Sergio E. Aguilar-Gutierrez
https://arxiv.org/abs/2506.21447 https://

Symmetry Sectors in Chord Space and Relational Holography in the DSSYK
Can there be multiple bulk theories for the same boundary theory? We answer this affirmatively in the double-scaled SYK (DSSYK) model using the tools of constrained systems. We find different symmetry sectors generated by specific constraints within the chord Hilbert space of the DSSYK with matter. Each sector corresponds to a different bulk description. These include chord parity symmetry, corresponding to End-Of-The-World (ETW) branes and Euclidean wormholes in sine dilaton gravity; and relat…

GANet-Seg: Adversarial Learning for Brain Tumor Segmentation with Hybrid Generative Models
Qifei Cui, Xinyu Lu
https://arxiv.org/abs/2506.21245 https://

GANet-Seg: Adversarial Learning for Brain Tumor Segmentation with Hybrid Generative Models
This work introduces a novel framework for brain tumor segmentation leveraging pre-trained GANs and Unet architectures. By combining a global anomaly detection module with a refined mask generation network, the proposed model accurately identifies tumor-sensitive regions and iteratively enhances segmentation precision using adversarial loss constraints. Multi-modal MRI data and synthetic image augmentation are employed to improve robustness and address the challenge of limited annotated dataset…

A Multi-Stage Framework for Multimodal Controllable Speech Synthesis
Rui Niu, Weihao Wu, Jie Chen, Long Ma, Zhiyong Wu
https://arxiv.org/abs/2506.20945 htt…

A Multi-Stage Framework for Multimodal Controllable Speech Synthesis
Controllable speech synthesis aims to control the style of generated speech using reference input, which can be of various modalities. Existing face-based methods struggle with robustness and generalization due to data quality constraints, while text prompt methods offer limited diversity and fine-grained control. Although multimodal approaches aim to integrate various modalities, their reliance on fully matched training data significantly constrains their performance and applicability. This pa…

This https://arxiv.org/abs/2501.00256 has been replaced.
initial toot: https://mastoxiv.page/@arX…

Rapid, High-resolution and Distortion-free $R_{2}^{*}$ Mapping of Fetal Brain using Multi-echo Radial FLASH and Model-based Reconstruction
Purpose: To develop a rapid, high-resolution and distortion-free technique for simultaneous water-fat separation, $R_{2}^{*}$ and $B_{0}$ mapping of the fetal brain at 3T. Methods: A 2D multi-echo radial FLASH sequence with blip gradients is adapted for data acquisition during maternal free breathing. A calibrationless model-based reconstruction with sparsity constraints is developed to jointly estimate water, fat, $R_{2}^{*}$ and $B_{0}$ field maps directly from k-space. This approach was va…

Optimal Parameter Design for Power Electronic Converters Using a Probabilistic Learning-Based Stochastic Surrogate Model
Akash Mahajan, Shivam Chaturvedi, Srijita Das, Wencong Su, Van-Hai Bui
https://arxiv.org/abs/2506.20987

Optimal Parameter Design for Power Electronic Converters Using a Probabilistic Learning-Based Stochastic Surrogate Model
The selection of optimal design for power electronic converter parameters involves balancing efficiency and thermal constraints to ensure high performance without compromising safety. This paper introduces a probabilistic-learning-based stochastic surrogate modeling framework to address this challenge and significantly reduce the time required during the design phase. The approach begins with a neural network classifier that evaluates the feasibility of parameter configurations, effectively fil…

Learning Lineage Constraints for Data Science Operations
Jinjin Zhao
https://arxiv.org/abs/2506.18252 https://arxiv.org/pdf/2506.1825…

Learning Lineage Constraints for Data Science Operations
Data science workflows often integrate functionalities from a diverse set of libraries and frameworks. Tasks such as debugging require data lineage that crosses library boundaries. The problem is that the way that "lineage" is represented is often intimately tied to particular data models and data manipulation paradigms. Inspired by the use of intermediate representations (IRs) in cross-library performance optimizations, this vision paper proposes a similar architecture for lineage - how do we …

Smoothness Meets Autobidding: Tight Price of Anarchy Bounds for Simultaneous First-Price Auctions
Riccardo Colini-Baldeschi, Sophie Klumper, Twan Kroll, Stefano Leonardi, Guido Sch\"afer, Artem Tsikiridis
https://arxiv.org/abs/2506.20908

Smoothness Meets Autobidding: Tight Price of Anarchy Bounds for Simultaneous First-Price Auctions
Online advertising systems have recently transitioned to autobidding, enabling advertisers to delegate bidding decisions to automated agents. Each advertiser directs their agent to optimize a valuation-dependent objective subject to return-on-investment (ROI) or budget constraints. Given their relevance, there has been a surge in literature studying the liquid welfare price of anarchy (POA) of core auction formats in autobidding, among which simultaneous first-price auctions (FPA). These models…

BAGELS: Benchmarking the Automated Generation and Extraction of Limitations from Scholarly Text
Ibrahim Al Azher, Miftahul Jannat Mokarrama, Zhishuai Guo, Sagnik Ray Choudhury, Hamed Alhoori
https://arxiv.org/abs/2505.18207

BAGELS: Benchmarking the Automated Generation and Extraction of Limitations from Scholarly Text
In scientific research, limitations refer to the shortcomings, constraints, or weaknesses within a study. Transparent reporting of such limitations can enhance the quality and reproducibility of research and improve public trust in science. However, authors often a) underreport them in the paper text and b) use hedging strategies to satisfy editorial requirements at the cost of readers' clarity and confidence. This underreporting behavior, along with an explosion in the number of publications, …

Inverse initial data reconstruction for Maxwell's equations via time-dimensional reduction method
Thuy T. Le, Cong B. Van, Trong D. Dang, Loc H. Nguyen
https://arxiv.org/abs/2506.20777

Inverse initial data reconstruction for Maxwell's equations via time-dimensional reduction method
We study an inverse problem for the time-dependent Maxwell system in an inhomogeneous and anisotropic medium. The objective is to recover the initial electric field $\mathbf{E}_0$ in a bounded domain $Ω\subset \mathbb{R}^3$, using boundary measurements of the electric field and its normal derivative over a finite time interval. Informed by practical constraints, we adopt an under-determined formulation of Maxwell's equations that avoids the need for initial magnetic field data and charge densi…

Sequential Quantum Computing
Sebasti\'an V. Romero, Alejandro Gomez Cadavid, Enrique Solano, Narendra N. Hegade
https://arxiv.org/abs/2506.20655 https:…

Sequential Quantum Computing
We propose and experimentally demonstrate sequential quantum computing (SQC), a paradigm that utilizes multiple homogeneous or heterogeneous quantum processors in hybrid classical-quantum workflows. In this manner, we are able to overcome the limitations of each type of quantum computer by combining their complementary strengths. Current quantum devices, including analog quantum annealers and digital quantum processors, offer distinct advantages, yet face significant practical constraints when …

Harnessing Higgs Kinematics for HEFT Constraints
Christoph Englert, Tom Ingebretsen Carlson, J\"orgen Sj\"olin, Michael Spannowsky
https://arxiv.org/abs/2506.19401

Harnessing Higgs Kinematics for HEFT Constraints
We present a momentum-dependent reweighting strategy to extend current LHC di-Higgs analyses within the $κ$-framework and SMEFT into the bosonic sector of the Higgs Effective Field Theory (HEFT). Unlike SMEFT, where symmetry constraints tightly correlate multi-Higgs processes, HEFT allows for a broader range of momentum-dependent deviations that can substantially impact di-Higgs kinematics and offer a powerful probe of non-linear Higgs dynamics. We generalise the interpretation of existing exp…

Situational-Constrained Sequential Resources Allocation via Reinforcement Learning
Libo Zhang, Yang Chen, Toru Takisaka, Kaiqi Zhao, Weidong Li, Jiamou Liu
https://arxiv.org/abs/2506.14125

Situational-Constrained Sequential Resources Allocation via Reinforcement Learning
Sequential Resource Allocation with situational constraints presents a significant challenge in real-world applications, where resource demands and priorities are context-dependent. This paper introduces a novel framework, SCRL, to address this problem. We formalize situational constraints as logic implications and develop a new algorithm that dynamically penalizes constraint violations. To handle situational constraints effectively, we propose a probabilistic selection mechanism to overcome li…

This https://arxiv.org/abs/2505.16768 has been replaced.
initial toot: https://mastoxiv.page/@arX…

High-Efficiency Plasma-Based Compressor for Ultrafast Soft X-ray Free-Electron Lasers
The generation of intense, femtosecond-scale X-ray pulses is crucial for probing matter under extreme temporal and field conditions. Current chirped-pulse amplification (CPA) techniques in free-electron lasers (FELs), however, face efficiency limitations in the soft X-ray regime due to the inherent constraints of conventional optical compressors. To address this challenge, we propose a high-efficiency plasma-based compressor utilizing highly ionized noble gas plasma. Exploiting strong refractiv…

10,000 Resolved Triples from Gaia: Empirical Constraints on Triple Star Populations
Cheyanne Shariat, Kareem El-Badry, Smadar Naoz
https://arxiv.org/abs/2506.16513

10,000 Resolved Triples from Gaia: Empirical Constraints on Triple Star Populations
We present a catalog of $\sim 10,000$ resolved triple star systems within 500 pc of the Sun, constructed using Gaia data. The triples include main-sequence, red giant, and white dwarf components spanning separations of 10 to 50,000 au. A well-characterized selection function allows us to constrain intrinsic demographics of the triple star population. We find that (a) all systems are compatible with being hierarchical and dynamically stable; (b) mutual orbital inclinations are isotropic for wide…

Can creativity and science-related work go together?
In this post I share four take-aways I had about creativity in relation to our work
​
​1: Innovation requires creativity.
​2: Creativity is in the little acts.
​3: Constraints can spark creativity.
​4: Experiment with saying “no” to rules.
http…

Creativity in our work ✨

Unconventional high-harmonic generation in resonant membrane metasurfaces
Pavel Tonkaev, Felix Richter, Ivan Toftul, Maria Antonietta Vincenti, Ivan Sinev, Michael Scalora, Hatice Altug, Yuri Kivshar
https://arxiv.org/abs/2506.20873

Unconventional high-harmonic generation in resonant membrane metasurfaces
High-harmonic generation (HHG) in solids has rapidly emerged as a promising platform for creating compact attosecond sources and probing ultrafast electron dynamics. Resonant metasurfaces are essential for enhancement of the otherwise small harmonic generation efficiency through local field enhancement and are essential to circumvent the need of phase matching constraints. Until now, the metasurface-enhanced HHG was believed to follow the conventional integer-power scaling laws that hold for no…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[3/4]:
- Iterative Minimax Games with Coupled Linear Constraints
Huiling Zhang, Zi Xu, Yu-Hong Dai

A Zeroth-Order Extra-Gradient Method For Black-Box Constrained Optimization
Yuke Zhou, Ruiyang Jin, Siyang Gao, Jianxiao Wang, Jie Song
https://arxiv.org/abs/2506.20546

A Zeroth-Order Extra-Gradient Method For Black-Box Constrained Optimization
Non-analytical objectives and constraints often arise in control systems, particularly in problems with complex dynamics, which are challenging yet lack efficient solution methods. In this work, we consider general constrained optimization problems involving black-box objectives and constraints. To solve it, we reformulate it as a min-max problem and propose a zeroth-order extra gradient (ZOEG) algorithm that combines the extra gradient method with a feedback-based stochastic zeroth-order gradi…

Non-Discriminatory Personalized Pricing
Philipp Strack, Kai Hao Yang
https://arxiv.org/abs/2506.20925 https://arxiv.org/pdf/2506.2092…

Non-Discriminatory Personalized Pricing
A monopolist offers personalized prices to consumers with unit demand, heterogeneous values, and idiosyncratic costs, who differ in a protected characteristic, such as race or gender. The seller is subject to a non-discrimination constraint: consumers with the same cost, but different characteristics must face identical prices. Such constraints arise in regulated markets like credit or insurance. The setting reduces to an optimal transport, and we characterize the optimal pricing rule. Under th…

Network Structures as an Attack Surface: Topology-Based Privacy Leakage in Federated Learning
Murtaza Rangwala, Richard O. Sinnott, Rajkumar Buyya
https://arxiv.org/abs/2506.19260

Network Structures as an Attack Surface: Topology-Based Privacy Leakage in Federated Learning
Federated learning systems increasingly rely on diverse network topologies to address scalability and organizational constraints. While existing privacy research focuses on gradient-based attacks, the privacy implications of network topology knowledge remain critically understudied. We conduct the first comprehensive analysis of topology-based privacy leakage across realistic adversarial knowledge scenarios, demonstrating that adversaries with varying degrees of structural knowledge can infer s…

X-ray and Radio Analysis of Abell 1644: Constraints on Cluster Dynamics
Humaira Bashir, R. Kale, Asif Iqbal, Manzoor A. Malik
https://arxiv.org/abs/2506.16197

X-ray and Radio Analysis of Abell 1644: Constraints on Cluster Dynamics
We present the first band-2 (120 - 250 MHz) uGMRT (upgraded Giant Metrewave Radio Telescope) observations of the bimodal galaxy cluster Abell 1644 (z = 0.0471), complemented by Chandra X-ray data. While weak lensing measurements reveal a third substructure in Abell 1644, our radio analysis reveals only two compact sources coinciding with the respective brightest cluster galaxies (BCGs) of the northern (A1644N) and southern (A1644S) substructures, seen in the X-ray observations. Radio analysis y…

Notes on Degeneracy and Robustness
Indrakshi Dey, Nicola Marchetti
https://arxiv.org/abs/2506.19974 https://arxiv.org/pdf/2506.19974

Notes on Degeneracy and Robustness
Degeneracy is the ability of structurally different elements to perform the same function or yield the same output under certain constraints. In contrast to redundancy, which implies identical backups, degeneracy allows diverse components to step in and perform the same or similar role. Mathematically, it is about mapping multiple distinct elements into the same function. In a degenerate system, failure in one part can be compensated by others not structurally linked. System functions are distr…

Dark Classification Matters: Searching for Primordial Black Holes with LSST
Miguel Crispim Romao, Djuna Croon, Benedict Crossey, Daniel Godines
https://arxiv.org/abs/2506.20709

Dark Classification Matters: Searching for Primordial Black Holes with LSST
We present projected constraints on the abundance of primordial black holes (PBHs) as a constituent of dark matter, based on microlensing observations from the upcoming Legacy Survey of Space and Time (LSST) at the Vera C. Rubin Observatory. We use a catalogue of microlensing light curves simulated with Rubin Observatory's OpSims to demonstrate that competitive constraints crucially rely on minimising the false positive rate (FPR) of the classification algorithm. We propose the Bayesian informa…

Optimizing Optimizations: Case Study on Detecting Specific Types of Mathematical Optimization Constraints with E-Graphs in JijModeling
Hiromi Ishii (Jij, Inc), Taro Shimizu (Jij, Inc), Toshiki Teramura (Jij, Inc)
https://arxiv.org/abs/2506.06495

Optimizing Optimizations: Case Study on Detecting Specific Types of Mathematical Optimization Constraints with E-Graphs in JijModeling
In solving mathematical optimization problems efficiently, it is crucial to make use of information about specific types of constraints, such as the one-hot or Special-Ordered Set (SOS) constraints. In many cases, exploiting such information gives asymptotically better execution time. JijModeling, an industrial-strength mathematical optimization modeller, achieves this by separating the symbolic representation of an optimization problem from the input data. In this paper, we will report a real-…

Worst-Case Symbolic Constraints Analysis and Generalisation with Large Language Models
Daniel Koh, Yannic Noller, Corina S. Pasareanu, Adrians Skapars, Youcheng Sun
https://arxiv.org/abs/2506.08171

Worst-Case Symbolic Constraints Analysis and Generalisation with Large Language Models
Large language models (LLMs) have been successfully applied to a variety of coding tasks, including code generation, completion, and repair. However, more complex symbolic reasoning tasks remain largely unexplored by LLMs. This paper investigates the capacity of LLMs to reason about worst-case executions in programs through symbolic constraints analysis, aiming to connect LLMs and symbolic reasoning approaches. Specifically, we define and address the problem of worst-case symbolic constraints a…

Careem, Uber's ride-hailing MENA arm, will suspend its decade-old Pakistan service on July 18, citing economic challenges, competition, and capital constraints (Ariba Shahid/Reuters)
https://www.reuters.com/world/asia-pacific

Virasoro constraints for Hodge integrals
Xin Wang
https://arxiv.org/abs/2506.10033 https://arxiv.org/pdf/2506.10033

Virasoro constraints for Hodge integrals
The purpose of this paper is to study Virasoro constraints for Hodge integrals in Gromov-Witten theory of any target varieties. Results consist of the following: Firstly, we propose Virasoro conjecture for Hodge integrals in Gromov-Witten theory of any target varieties; Secondly, we prove Virasoro constraints for Hodge integrals in genus zero of any target varieties; Thirdly, we prove the genus-1 $L_1^{\mathbb{E}}$ constraint with one Hodge character class insertion for any target varieties; La…

The Sample Complexity of Distributed Simple Binary Hypothesis Testing under Information Constraints
Hadi Kazemi, Ankit Pensia, Varun Jog
https://arxiv.org/abs/2506.13686

The Sample Complexity of Distributed Simple Binary Hypothesis Testing under Information Constraints
This paper resolves two open problems from a recent paper, arXiv:2403.16981, concerning the sample complexity of distributed simple binary hypothesis testing under information constraints. The first open problem asks whether interaction reduces the sample complexity of distributed simple binary hypothesis testing. In this paper, we show that sequential interaction does not help. The second problem suggests tightening existing sample complexity bounds for communication-constrained simple binary …

Gravitational Baryogenesis Constraints on Nojiri-Odintsov $f(R)$ Gravity
Kalyan Malakar, Mrinnoy M Gohain, Kalyan Bhuyan
https://arxiv.org/abs/2506.18949 h…

Gravitational Baryogenesis Constraints on Nojiri-Odintsov $f(R)$ Gravity
This paper focuses on exploring the imbalance between matter and antimatter via the gravitational baryogenesis mechanism within the framework of Nojiri-Odintsov $f(R)$ gravity models in a spatially flat FLRW universe, where $R$ is the Ricci curvature scalar. This mechanism is based on \(\mathcal{CP}\)-violation generated via introducing an interaction between baryonic matter current (\(J^μ\)) and the derivative of curvature scalar (\(\partial_μR\)), which finally results in the baryon asymmet…

Reducing Self-Interaction Error in Transition-Metal Oxides with Different Exact-Exchange Fractions for Energy and Density
Harshan Reddy Gopidi, Ruiqi Zhang, Yanyong Wang, Abhirup Patra, Jianwei Sun, Adrienn Ruzsinszky, John P. Perdew, Pieremanuele Canepa
https://arxiv.org/abs/2506.20635 …

Reducing Self-Interaction Error in Transition-Metal Oxides with Different Exact-Exchange Fractions for Energy and Density
DFT is vital for materials discovery, and at the base of extensive molecular and materials databases, chemical reaction predictions, and machine learning potentials. The widespread use of DFT in chemistry and materials science aims for "chemical accuracy," but this is limited by the unknown exchange and correlation (XC) functional. A meta-GGA, the restored regularized strongly constrained and appropriately normed, r r2SCAN XC functional, fulfils 17 exact constraints of the XC energy. r2SCAN sti…

Staggered nonlinear spin generations in centrosymmetric altermagnets under electric current
Jie Zhang, Ruijing Fang, Zhichao Zhou, Xiao Li
https://arxiv.org/abs/2506.20298

Staggered nonlinear spin generations in centrosymmetric altermagnets under electric current
Current-induced spin generations are of significant importance for electrically controllable magnetization. Due to symmetry constraints, linear spin generation is absent in centrosymmetric magnets and nonlinear contributions become crucial. However, nonlinear spin generations have few examples in centrosymmetric compensated magnets with opposite-spin sublattices, which hinders electric control of associated magnetization. Here, we study nonlinear spin generations in altermagnets with opposite-s…

Truncation is not a #content strategy

"EV battery recycling key to easing future lithium supply constraints, modeling study shows"
#EV #ElectricVehicle #Cars

EV battery recycling key to easing future lithium supply constraints, modeling study shows
Lightweight, powerful lithium-ion batteries are crucial for the transition to electric vehicles, and global demand for lithium is set to grow rapidly over the next 25 years. A new analysis from the University of California, Davis, published May 29 in Nature Sustainability, looks at how new mining operations and battery recycling could meet that demand. Recycling could play a big role in easing supply constraints, the researchers found.

Physics-Informed Neural Networks for Industrial Gas Turbines: Recent Trends, Advancements and Challenges
Afila Ajithkumar Sophiya, Sepehr Maleki, Giuseppe Bruni, Senthil K. Krishnababu
https://arxiv.org/abs/2506.19503

Physics-Informed Neural Networks for Industrial Gas Turbines: Recent Trends, Advancements and Challenges
Physics-Informed Neural Networks (PINNs) have emerged as a promising computational framework for solving differential equations by integrating deep learning with physical constraints. However, their application in gas turbines is still in its early stages, requiring further refinement and standardization for wider adoption. This survey provides a comprehensive review of PINNs in Industrial Gas Turbines (IGTs) research, highlighting their contributions to the analysis of aerodynamic and aeromech…

Low-Energy Supernova Constraints on Lepton Flavor Violating Axions
Zi-Miao Huang, Zuowei Liu
https://arxiv.org/abs/2506.16922 https://

Low-Energy Supernova Constraints on Lepton Flavor Violating Axions
The extreme conditions within the supernova core, a high-temperature and high-density environment, create an ideal laboratory for the search for new physics beyond the Standard Model. Of particular interest are low-energy supernovae, characterized by their low explosion energies, which place strong constraints on the new-physics energy transfer from the core to the mantle. We compute low-energy supernova constraints on lepton-flavor-violating axions and axion-like particles that couple to both …

Adversarial Observability and Performance Tradeoffs in Optimal Control
Filippos Fotiadis, Ufuk Topcu
https://arxiv.org/abs/2506.19829 https://

Adversarial Observability and Performance Tradeoffs in Optimal Control
We develop a feedback controller that minimizes the observability of a set of adversarial sensors of a linear system, while adhering to strict closed-loop performance constraints. We quantify the effectiveness of adversarial sensors using the trace of their observability Gramian and its inverse, capturing both average observability and the least observable state directions of the system. We derive theoretical lower bounds on these metrics under performance constraints, characterizing the fundam…

Near Time-Optimal Hybrid Motion Planning for Timber Cranes
Marc-Philip Ecker, Bernhard Bischof, Minh Nhat Vu, Christoph Fr\"ohlich, Tobias Gl\"uck, Wolfgang Kemmetm\"uller
https://arxiv.org/abs/2506.20314

Near Time-Optimal Hybrid Motion Planning for Timber Cranes
Efficient, collision-free motion planning is essential for automating large-scale manipulators like timber cranes. They come with unique challenges such as hydraulic actuation constraints and passive joints-factors that are seldom addressed by current motion planning methods. This paper introduces a novel approach for time-optimal, collision-free hybrid motion planning for a hydraulically actuated timber crane with passive joints. We enhance the via-point-based stochastic trajectory optimizatio…

New bounds on Memory Burdened Primordial Black Holes from Big Bang Nucleosynthesis
Arnab Chaudhuri, Kazunori Kohri, Valentin Thoss
https://arxiv.org/abs/2506.20717

New bounds on Memory Burdened Primordial Black Holes from Big Bang Nucleosynthesis
Primordial black holes (PBHs) with masses below $10^9\,\rm{g}$ are typically assumed to have negligible cosmological impact due to their rapid evaporation via Hawking radiation. However, the 'memory burden' effect, which is a quantum suppression of PBH evaporation, can dramatically alter their decay dynamics. In this work, we revisit early-Universe constraints on ultralight PBHs in this mass range, demonstrating that memory burden significantly alters previous constraints. We compute new cosmol…

Linear and nonlinear supersymmetry in field and string theory
Gabriele Casagrande
https://arxiv.org/abs/2506.20396 https://arxiv.org/…

Linear and nonlinear supersymmetry in field and string theory
This Ph.D. thesis investigates effective field and string theories in which supersymmetry is realized and broken in various ways. Chapter 1 addresses effective theories with nonlinearly realized supersymmetry, constructed using the formalism of constrained superfields. We establish a criterion for identifying inconsistent constraints, formulate their improved versions, and propose, along these lines, improved supergravity models of inflation. In Chapter 2, we discuss a peculiar application of t…

Linear and Second-order-cone Valid Inequalities for Problems with Storage
Juan M. Morales
https://arxiv.org/abs/2506.21470 https://ar…

Linear and Second-order-cone Valid Inequalities for Problems with Storage
Batteries are playing an increasingly central role as distributed energy resources in the shift toward power systems dominated by renewable energy sources. However, existing battery models must invariably rely on complementarity constraints to prevent simultaneous charging and discharging, rendering models of a disjunctive nature and NP-hard. In this paper, we analyze the disjunctive structure of the battery's feasible operational set and uncover a submodularity property in its extreme power tr…

Dissipative quantum North-East-Center model: steady-state phase diagram, universality and nonergodic dynamics
Pietro Brighi, Alberto Biella
https://arxiv.org/abs/2506.19011

Dissipative quantum North-East-Center model: steady-state phase diagram, universality and nonergodic dynamics
In this work we study the dissipative quantum North-East-Center (NEC) model: a two-dimensional spin-1/2 lattice subject to chiral, kinetically constrained dissipation and coherent quantum interactions. This model combines kinetic constraints and chirality at the dissipative level, implementing local incoherent spin flips conditioned by an asymmetric majority-vote rule. Through a cluster mean-field approach, we determine the steady-state phase diagram of the NEC model under different Hamiltonian…

Line Aspect Ratio
Arash Vaezi
https://arxiv.org/abs/2506.20508 https://arxiv.org/pdf/2506.20508…

Line Aspect Ratio
We address the problem of covering a target segment $\overline{uv}$ using a finite set of guards $\mathcal{S}$ placed on a source segment $\overline{xy}$ within a simple polygon $\mathcal{P}$, assuming weak visibility between the target and source. Without geometric constraints, $\mathcal{S}$ may be infinite, as shown by prior hardness results. To overcome this, we introduce the {\it line aspect ratio} (AR), defined as the ratio of the \emph{long width} (LW) to the \emph{short width} (SW) of $\…

T-CPDL: A Temporal Causal Probabilistic Description Logic for Developing Logic-RAG Agent
Hong Qing Yu
https://arxiv.org/abs/2506.18559 https://

T-CPDL: A Temporal Causal Probabilistic Description Logic for Developing Logic-RAG Agent
Large language models excel at generating fluent text but frequently struggle with structured reasoning involving temporal constraints, causal relationships, and probabilistic reasoning. To address these limitations, we propose Temporal Causal Probabilistic Description Logic (T-CPDL), an integrated framework that extends traditional Description Logic with temporal interval operators, explicit causal relationships, and probabilistic annotations. We present two distinct variants of T-CPDL: one ca…

Investigation of the neutron-proton effective mass splitting via heavy ion collisions: Constraints and Implications
Junping Yang, Meiqi Sun, Ying Cui, Yangyang Liu, Zhuxia Li, Kai Zhao, Yingxun Zhang
https://arxiv.org/abs/2506.17973

Investigation of the neutron-proton effective mass splitting via heavy ion collisions: Constraints and Implications
The neutron-proton effective mass splitting ($Δm^*_{np}$) is investigated through analyses of heavy-ion collisions using the improved quantum molecular dynamics (ImQMD) model with both standard and extended Skyrme interactions. By uncovering the strong correlation between the slope of the neutron-to-proton yield ratio with respect to the kinetic energy (i.e., $S_{n/p} $) and $Δm^*_{np}$, we reveal that the constraints of the neutron-proton effective mass splitting via heavy ion collisions dep…

Beyond Prediction -- Structuring Epistemic Integrity in Artificial Reasoning Systems
Craig Steven Wright
https://arxiv.org/abs/2506.17331 https://

Beyond Prediction -- Structuring Epistemic Integrity in Artificial Reasoning Systems
This paper develops a comprehensive framework for artificial intelligence systems that operate under strict epistemic constraints, moving beyond stochastic language prediction to support structured reasoning, propositional commitment, and contradiction detection. It formalises belief representation, metacognitive processes, and normative verification, integrating symbolic inference, knowledge graphs, and blockchain-based justification to ensure truth-preserving, auditably rational epistemic age…

A Taylor-Hood finite element method for the surface Stokes problem without penalization
Alan Demlow, Michael Neilan
https://arxiv.org/abs/2506.20419 https:…

A Taylor-Hood finite element method for the surface Stokes problem without penalization
Finite element approximation of the velocity-pressure formulation of the surfaces Stokes equations is challenging because it is typically not possible to enforce both tangentiality and $H^1$ conformity of the velocity field. Most previous works concerning finite element methods (FEMs) for these equations thus have weakly enforced one of these two constraints by penalization or a Lagrange multiplier formulation. Recently in [A tangential and penalty-free finite element method for the surface Sto…

Experimental demonstration of high compression of space by optical spaceplates
Ryan Hogan, Yaryna Mamchur, R. Margoth Cordova-Castro, Graham Carlow, Brian T. Sullivan, Orad Reshef, Robert W. Boyd, Jeff S. Lundeen
https://arxiv.org/abs/2506.20647

Experimental demonstration of high compression of space by optical spaceplates
The physical size of optical imaging systems is one of the greatest constraints on their use, limiting the performance and deployment of a range of systems from telescopes to mobile phone cameras. Spaceplates are nonlocal optical devices that compress free-space propagation into a shorter distance, paving the way for more compact optical systems, potentially even thin flat cameras. Here, we demonstrate the first engineered optical spaceplate and experimentally observe the highest space compress…

Communication-Aware Map Compression for Online Path-Planning: A Rate-Distortion Approach
Ali Reza Pedram, Evangelos Psomiadis, Dipankar Maity, Panagiotis Tsiotras
https://arxiv.org/abs/2506.20579

Communication-Aware Map Compression for Online Path-Planning: A Rate-Distortion Approach
This paper addresses the problem of collaborative navigation in an unknown environment, where two robots, referred to in the sequel as the Seeker and the Supporter, traverse the space simultaneously. The Supporter assists the Seeker by transmitting a compressed representation of its local map under bandwidth constraints to support the Seeker's path-planning task. We introduce a bit-rate metric based on the expected binary codeword length to quantify communication cost. Using this metric, we for…

On reversible and reversible-complementary DNA codes over $\mathbb{F}_{4}$
E. J. Garc\'ia-Claro
https://arxiv.org/abs/2506.19170 https://

On reversible and reversible-complementary DNA codes over $\mathbb{F}_{4}$
A method to construct and count all the linear codes (of arbitrary length) in $\mathbb{F}_{4}$ that are invariant under reverse permutation and that contain the repetition code is presented. These codes are suitable for constructing DNA codes that satisfy the reverse and reverse-complement constraints. By analyzing a module-theoretic structure of these codes, their generating matrices are characterized in terms of their isomorphism type, and explicit formulas for counting them are provided. The…

OCPQ: Object-Centric Process Querying & Constraints
Aaron K\"usters, Wil M. P. van der Aalst
https://arxiv.org/abs/2506.11541 https://

OCPQ: Object-Centric Process Querying & Constraints
Process querying is used to extract information and insights from process execution data. Similarly, process constraints can be checked against input data, yielding information on which process instances violate them. Traditionally, such process mining techniques use case-centric event data as input. However, with the uptake of Object-Centric Process Mining (OCPM), existing querying and constraint checking techniques are no longer applicable. Object-Centric Event Data (OCED) removes the require…

Deformable Medical Image Registration with Effective Anatomical Structure Representation and Divide-and-Conquer Network
Xinke Ma, Yongsheng Pan, Qingjie Zeng, Mengkang Lu, Bolysbek Murat Yerzhanuly, Bazargul Matkerim, Yong Xia
https://arxiv.org/abs/2506.19222

Deformable Medical Image Registration with Effective Anatomical Structure Representation and Divide-and-Conquer Network
Effective representation of Regions of Interest (ROI) and independent alignment of these ROIs can significantly enhance the performance of deformable medical image registration (DMIR). However, current learning-based DMIR methods have limitations. Unsupervised techniques disregard ROI representation and proceed directly with aligning pairs of images, while weakly-supervised methods heavily depend on label constraints to facilitate registration. To address these issues, we introduce a novel ROI-…

Hierarchical constraints on gravitational waves from horizonless compact objects
Rajrupa Mondal, Julian Westerweck, Yotam Sherf, Collin D. Capano
https://arxiv.org/abs/2506.17215 …

Hierarchical constraints on gravitational waves from horizonless compact objects
We use the data of several promising gravitational wave observations to obtain increasingly stringent bounds on near-horizon deviations of their sources from the Kerr geometry. A range of horizonless compact objects proposed as alternatives to black holes of general relativity would possess a modified gravitational wave emission after the merger. Modelling these objects by introducing reflection of gravitational waves near the horizon, we can measure deviations from Kerr in terms of a single ad…

Coupled Entropy: A Goldilocks Generalization?
Kenric P. Nelson
https://arxiv.org/abs/2506.17229 https://arxiv.org/pdf/2506.17229

Coupled Entropy: A Goldilocks Generalization?
Nonextensive Statistical Mechanics (NSM) has developed into a powerful toolset for modeling and analyzing complex systems. Despite its many successes, a puzzle arose early in its development. The constraints on the Tsallis entropy are in the form of an escort distribution with elements proportional to $p_i^q$, but this same factor within the Tsallis entropy function is not normalized. This led to consideration of the Normalized Tsallis Entropy (NTE); however, the normalization proved to make th…

Understanding the Theoretical Guarantees of DPM
Yara Sch\"utt, Esfandiar Mohammadi
https://arxiv.org/abs/2506.18685 https://arxi…

Understanding the Theoretical Guarantees of DPM
In this study, we conducted an in-depth examination of the utility analysis of the differentially private mechanism (DPM). The authors of DPM have already established the probability of a good split being selected and of DPM halting. In this study, we expanded the analysis of the stopping criterion and provided an interpretation of these guarantees in the context of realistic input distributions. Our findings revealed constraints on the minimum cluster size and the metric weight for the scoring…

A quadratic Abramovich-Bertram formula
Erwan Brugall\'e, Kirsten Wickelgren
https://arxiv.org/abs/2506.17854 https://arxiv.org/pd…

A quadratic Abramovich-Bertram formula
Quadratic Gromov--Witten invariants allow one to obtain an arithmetically meaningful count of curves satisfying constraints over a field $k$ without assuming that $k$ is the field of complex or real numbers. This paper studies the behavior of quadratic genus $0$ Gromov--Witten invariants during an algebraic analogue of surgery on del Pezzo surfaces. For this, we define and study (twisted) binomial coefficients in the Grothendieck--Witt group, building on work of Serre. We obtain a formula expre…

Convergent Proximal Multiblock ADMM for Nonconvex Dynamics-Constrained Optimization
Bowen Li, Ya-xiang Yuan
https://arxiv.org/abs/2506.17405 https://

Convergent Proximal Multiblock ADMM for Nonconvex Dynamics-Constrained Optimization
This paper proposes a provably convergent multiblock ADMM for nonconvex optimization with nonlinear dynamics constraints, overcoming the divergence issue in classical extensions. We consider a class of optimization problems that arise from discretization of dynamics-constrained variational problems that are optimization problems for a functional constrained by time-dependent ODEs or PDEs. This is a family of $n$-sum nonconvex optimization problems with nonlinear constraints. We study the conver…

Observational Constraints on Scalar Field--Matter Interaction in Weyl Integrable Spacetime
Andronikos Paliathanasis
https://arxiv.org/abs/2506.16223 https:…

Observational Constraints on Scalar Field--Matter Interaction in Weyl Integrable Spacetime
We test an analytic cosmological solution within the framework of Weyl Integrable Spacetime using current observational data. In this model, dark energy is described by a pressureless fluid, while a scalar field arises naturally through the definition of the connection. This gravitational theory reveals a Chameleon Mechanism leading to a nonzero interaction between the scalar field and the matter sector. This model extends the standard $Λ$CDM cosmology by introducing one additional degree of f…

Continuous spin superparticle model
I. L. Buchbinder, S. A. Fedoruk
https://arxiv.org/abs/2506.19709 https://arxiv.org/pdf/2506.19709…

Continuous spin superparticle model
We construct a new model of a particle propagating in $4D$, ${\cal N}=1$ superspace that describes the dynamics of a continuous spin irreducible representation of the Poincaré supergroup. The model is characterized by two-component Weyl spinor additional even variables playing the role of extra coordinates. A canonical formulation, specific local fermionic $κ$-symmetry, and a compete system of bosonic and fermionic constraints are derived. All bosonic constrains are first-class, while fermion…

Robust Adaptive Time-Varying Control Barrier Function with Application to Robotic Surface Treatment
Yitaek Kim, Christoffer Sloth
https://arxiv.org/abs/2506.14249

Robust Adaptive Time-Varying Control Barrier Function with Application to Robotic Surface Treatment
Set invariance techniques such as control barrier functions (CBFs) can be used to enforce time-varying constraints such as keeping a safe distance from dynamic objects. However, existing methods for enforcing time-varying constraints often overlook model uncertainties. To address this issue, this paper proposes a CBFs-based robust adaptive controller design endowing time-varying constraints while considering parametric uncertainty and additive disturbances. To this end, we first leverage Robust…

Impact of $\Xi$-Hypernuclear Constraints on Relativistic Equation of States and Properties of Hyperon Stars
Shi-Yuan Ding, Xiang-Dong Sun, Bao-Yuan Sun, Ang Li
https://arxiv.org/abs/2506.16871

Impact of $Ξ$-Hypernuclear Constraints on Relativistic Equation of States and Properties of Hyperon Stars
Significant uncertainties persist in describing the equation of state and internal structure of hyperon stars due to the limited understanding of the mechanisms underlying hyperon interactions. Constraining the interaction parameter space through a combination of the latest astronomical observations and hypernuclear physics experiments is therefore essential. In this study, we incorporate experimental constraints from $Ξ$ hypernuclear physics on top of $Λ$ hyperons considered in \citet{Sun202…

Optimal quantum metrology under energy constraints
Longyun Chen, Yuxiang Yang
https://arxiv.org/abs/2506.09436 https://arxiv.org/pdf/…

Optimal quantum metrology under energy constraints
Quantum metrology promises enhanced precision in parameter estimation by exploiting quantum effects. The traditional framework of metrology commonly assumes unlimited access to resources, overlooking resource constraints in realistic scenarios. As such, the optimal strategies therein are often infeasible in practice. Here, we investigate quantum metrology where the total energy consumption of the probe state preparation, intermediate control operations, and the final measurement is subject to a…

Self-Interacting Dark-Matter Spikes and the Final-Parsec Problem: Bayesian constraints from the NANOGrav 15-Year Gravitational-Wave Background
Shreyas Tiruvaskar, Chris Gordon
https://arxiv.org/abs/2506.18153

Self-Interacting Dark-Matter Spikes and the Final-Parsec Problem: Bayesian constraints from the NANOGrav 15-Year Gravitational-Wave Background
A self-interacting dark-matter (SIDM) density spike around merging supermassive black holes (SMBHs) may be able to supply the dynamical friction needed to shrink binaries from $\sim 1\, \mathrm{pc}$ to $\sim 10^{-2} \,\mathrm{pc}$, thereby resolving the long-standing ``final-parsec problem.'' Embedding the binary-halo system in a cosmological population model, we evolve the inspiral under the combined influence of gravitational-wave (GW) emission and SIDM drag, compute the resulting nanohertz G…

Time-Constrained Interception of Seeker-Equipped Interceptors with Bounded Input
Ashok Samrat R, Swati Singh, Shashi Ranjan Kumar
https://arxiv.org/abs/2506.19522

Time-Constrained Interception of Seeker-Equipped Interceptors with Bounded Input
This paper presents a nonlinear guidance scheme designed to achieve precise interception of stationary targets at a pre-specified impact time. The proposed strategy essentially accounts for the constraints imposed by the interceptor's seeker field-of-view (FOV) and actuator limitations, which, if ignored, can degrade guidance performance. To address these challenges, the guidance law incorporates known actuator bounds directly into its design, thereby improving overall interceptor effectiveness…

A polynomial projective algorithm for convex feasibility problems with positive-definite constraints
Sergei Chubanov
https://arxiv.org/abs/2506.15484 https…

A polynomial projective algorithm for convex feasibility problems with positive-definite constraints
We study a class of projective transformations of spectraplexes associated with self-dual cones and, on this basis, propose a polynomial-time algorithm for convex feasibility problems with positive definite constraints. At each iteration of the algorithm, either a feasible solution is found or a suitable valid inequality inducing a projective transformation allowing to bring the solution set closer to the center of an associated spectraplex. The closeness to the center is measured in terms of a…

Higher co-dimension de Sitter branes
Florian Niedermann, Antonio Padilla
https://arxiv.org/abs/2506.19515 https://arxiv.org/pdf/2506.…

Higher co-dimension de Sitter branes
We extend the arguments of Maldacena and Núñez to include higher co-dimension brane setups and derive a new no-go theorem. Specifically, we show that under reasonable assumptions on the energy-momentum conservation and the bulk curvature, co-dimension-two branes fail to support stable de Sitter solutions. For co-dimensions higher than two embedded in a compact internal space, we show that negative tension sources would be required. This result places strong constraints on the viability of hig…

Hierarchical Learning-Enhanced MPC for Safe Crowd Navigation with Heterogeneous Constraints
Huajian Liu, Yixuan Feng, Wei Dong, Kunpeng Fan, Chao Wang, Yongzhuo Gao
https://arxiv.org/abs/2506.09859

Hierarchical Learning-Enhanced MPC for Safe Crowd Navigation with Heterogeneous Constraints
In this paper, we propose a novel hierarchical framework for robot navigation in dynamic environments with heterogeneous constraints. Our approach leverages a graph neural network trained via reinforcement learning (RL) to efficiently estimate the robot's cost-to-go, formulated as local goal recommendations. A spatio-temporal path-searching module, which accounts for kinematic constraints, is then employed to generate a reference trajectory to facilitate solving the non-convex optimization prob…

Self-consistent population synthesis of AGN from observational constraints in the X-rays
D. Gerolymatou, S. Paltani, C. Ricci, M. Regamey
https://arxiv.org/abs/2506.14885

Self-consistent population synthesis of AGN from observational constraints in the X-rays
The cosmic X-ray background (CXB) is produced by the emission of unresolved active galactic nuclei (AGN), thus providing key information about the properties of the primary and reprocessed X-ray emission components of the AGN population. Equally important, studies of individual sources provide additional constraints on the properties of AGN, such as their luminosity and obscuration. Until now, these constraints have not been self-consistently addressed by intrinsically linking emission, absorpt…

WIMPs Below the Radar: Blind Spots and Benchmarks Beyond the Neutrino Floor
Giorgio Arcadi, Stefano Profumo
https://arxiv.org/abs/2506.19062 https://

WIMPs Below the Radar: Blind Spots and Benchmarks Beyond the Neutrino Floor
We investigate benchmark scenarios for Weakly Interacting Massive Particles (WIMPs) that naturally evade current direct detection constraints by featuring suppressed spin-independent cross-sections. Focusing on three representative models, the Singlet-Doublet fermion model, its extension to a Two-Higgs-Doublet plus pseudoscalar sector (2HDM$+a$), and a dark $SU(3)$ gauge model, we systematically analyze the interplay between thermal freeze-out, direct detection blind spots, and radiative correc…

Optimization over Sparse Support-Preserving Sets: Two-Step Projection with Global Optimality Guarantees
William de Vazelhes, Xiao-Tong Yuan, Bin Gu
https://arxiv.org/abs/2506.08558

Optimization over Sparse Support-Preserving Sets: Two-Step Projection with Global Optimality Guarantees
In sparse optimization, enforcing hard constraints using the $\ell_0$ pseudo-norm offers advantages like controlled sparsity compared to convex relaxations. However, many real-world applications demand not only sparsity constraints but also some extra constraints. While prior algorithms have been developed to address this complex scenario with mixed combinatorial and convex constraints, they typically require the closed form projection onto the mixed constraints which might not exist, and/or on…

Spin-Dependent Scattering of Sub-GeV Dark Matter: Models and Constraints
Stefania Gori, Simon Knapen, Tongyan Lin, Pankaj Munbodh, Bethany Suter
https://arxiv.org/abs/2506.11191

Spin-Dependent Scattering of Sub-GeV Dark Matter: Models and Constraints
We calculate the scattering rate of sub-GeV dark matter in solid-state targets for spin-dependent dark matter -- nucleon interactions. For dark matter particles with mass below 100 MeV, the scattering occurs predominantly through incoherent phonon production. For dark matter heavier than 100 MeV, we match onto the nuclear recoil calculation. To compare the sensitivity of future direct detection experiments with existing constraints, we consider three models with interactions which are mediated …

Integrating Maneuverable Planning and Adaptive Control for Robot Cart-Pushing under Disturbances
Zhe Zhang, Peijia Xie, Zhirui Sun, Bingyi Xia, Bi-Ke Zhu, Jiankun Wang
https://arxiv.org/abs/2506.18410

Integrating Maneuverable Planning and Adaptive Control for Robot Cart-Pushing under Disturbances
Precise and flexible cart-pushing is a challenging task for mobile robots. The motion constraints during cart-pushing and the robot's redundancy lead to complex motion planning problems, while variable payloads and disturbances present complicated dynamics. In this work, we propose a novel planning and control framework for flexible whole-body coordination and robust adaptive control. Our motion planning method employs a local coordinate representation and a novel kinematic model to solve a non…

Bregman proximal gradient method for linear optimization under entropic constraints
Luis M. Brice\~no-Arias, Ma\"el Le Treust
https://arxiv.org/abs/2506.10849

Bregman proximal gradient method for linear optimization under entropic constraints
In this paper, we present an efficient algorithm for solving a linear optimization problem with entropic constraints -- a class of problems that arises in game theory and information theory. Our analysis distinguishes between the cases of active and inactive constraints, addressing each using a Bregman proximal gradient method with entropic Legendre functions, for which we establish an ergodic convergence rate of $O(1/n)$ in objective values. For a specific cost structure, our framework provide…

PG-LIO: Photometric-Geometric fusion for Robust LiDAR-Inertial Odometry
Nikhil Khedekar, Kostas Alexis
https://arxiv.org/abs/2506.18583 https://

PG-LIO: Photometric-Geometric fusion for Robust LiDAR-Inertial Odometry
LiDAR-Inertial Odometry (LIO) is widely used for accurate state estimation and mapping which is an essential requirement for autonomous robots. Conventional LIO methods typically rely on formulating constraints from the geometric structure sampled by the LiDAR. Hence, in the lack of geometric structure, these tend to become ill-conditioned (degenerate) and fail. Robustness of LIO to such conditions is a necessity for its broader deployment. To address this, we propose PG-LIO, a real-time LIO me…

Replaced article(s) found for math.OC. https://arxiv.org/list/math.OC/new
[1/1]:
- Iterative Minimax Games with Coupled Linear Constraints
Huiling Zhang, Zi Xu, Yu-Hong Dai

Learning Physical Systems: Symplectification via Gauge Fixing in Dirac Structures
Aristotelis Papatheodorou, Pranav Vaidhyanathan, Natalia Ares, Ioannis Havoutis
https://arxiv.org/abs/2506.18812

Learning Physical Systems: Symplectification via Gauge Fixing in Dirac Structures
Physics-informed deep learning has achieved remarkable progress by embedding geometric priors, such as Hamiltonian symmetries and variational principles, into neural networks, enabling structure-preserving models that extrapolate with high accuracy. However, in systems with dissipation and holonomic constraints, ubiquitous in legged locomotion and multibody robotics, the canonical symplectic form becomes degenerate, undermining the very invariants that guarantee stability and long-term predicti…

Safety-Aware Optimal Scheduling for Autonomous Masonry Construction using Collaborative Heterogeneous Aerial Robots
Marios-Nektarios Stamatopoulos, Shridhar Velhal, Avijit Banerjee, George Nikolakopoulos
https://arxiv.org/abs/2506.18697

Safety-Aware Optimal Scheduling for Autonomous Masonry Construction using Collaborative Heterogeneous Aerial Robots
This paper presents a novel high-level task planning and optimal coordination framework for autonomous masonry construction, using a team of heterogeneous aerial robotic workers, consisting of agents with separate skills for brick placement and mortar application. This introduces new challenges in scheduling and coordination, particularly due to the mortar curing deadline required for structural bonding and ensuring the safety constraints among UAVs operating in parallel. To address this, an au…